Table 10 NIS-related DEA studies
Article | Method | Variables | |
|---|---|---|---|
Inputs | Outputs | ||
Matei & Aldea, 2012 | DEA Innovation leaders; Innovation followers; Moderate innovators; Modest innovators | • New doctorate graduates (ISCED 6) per 1000 population. • International scientific co-publications per million population. • Public R&D expenditures as % of GDP. • Business R&D expenditures as % of GDP. • patents applications per billion GDP. trademarks per billion GDP • Trademarks per billion GDP. | • Employment in knowledge-intensive activities (manufacturing and services) as % of total employment. • Medium and high-tech product exports as % total product exports. • Knowledge-intensive services exports as % total service exports. |
Guan & Chen, 2010 | CRS- output oriented Two stages DEA process | • R&D expenditure. • Technology import. | • Patent applications. • High-tech export. |
Lee & Park, 2005 | DEA The output oriented CCR model + Clustering + Anova—ANOVA and Post-hoc Comparisons inventors, merchandisers, academicians, and duds | • R&D expenditure. • Average number of researchers. | • Technology balance of receipts. • Number of scientific and technical journal articles. • Number of triadic patent families. |
Guan & Chen, 2012 | DEA CRS and VRS, Network (2-stage)-output oriented Super efficiency + Tobit regression on environmental factors | • Number of full-time equivalent scientists and engineers. • Incremental R&D expenditure funding. • Innovation activities. • Prior accumulated knowledge stock breeding upstream knowledge production. • Consumed full-time equivalent labour for non-R&D activities. • Number of patents granted. | • Number of patents granted. • International scientific papers. • Added value of industries. • Export of new products in high-tech industries. |
Lu et al., 2014 | Network DEA | • Total R&D personnel. • Public expenditures on education. • Import of goods and commercial services. • Total expenditures on R&D. | • GDP • Published scientific articles. • Patents (residents and nonresidents). |
Carayannis et al., 2015 | VRS-multistage, multilevel (2 stages x 2 levels) | • Science graduates in tertiary education. • Participation in lifelong learning. • Total R&D expenditure. • R&D capital stock. • Citable documents. • Patent applications. • Employment in knowledge intensive services/manufacturing. • SMEs collaborating with others. • Venture capital investment. | • High Tech Exports. • Sales of new to market and new to firm innovation. • License and patent revenues from abroad. • Number of trademark applications in national offices. |
Wang & Huang, 2007 | Three-stage approach Input-oriented DEA – BCC; Tobit regressions; Parameter estimates from the second stage are used to predict the total input slacks. | • GERD. • Fixed capital formation. • Researchers. • Technicians | • Patents. • SCI Papers. • EI Papers. |
Chen et al., 2011 | DEA–output-oriented- CRS | • Total R&D manpower. • R&D expenditure stocks. | • Patents. • Scientific journal articles. • Royalty and licensing fees. |
Pan et al., 2010 | Input- oriented DEA model | • Total public expenditure on education. • Imports of goods and commercial services. • Total expenditure on R&D. • Direct investment stocks abroad. • Total R&D personnel nationwide. | • Number of patents granted to residents. • Number of patents secured abroad by national residents. • Scientific articles published by origin of author. |
Cai, 2011 | DEA + OLS Regression | • R&D expenditure as a % of GDP. • Total R&D personnel. | • Patents per 1000 population. • Scientific articles per 1000 population. • High-tech exports as a % of total manufacturing exports. |
Afzal, 2014 | Output- oriented DEA- CRS + Tobit regression model | • Population ages 15 to 65 (% of total) as labour force. • Computer users per 1000. • Domestic credit provided by banking sector (% of GDP). • R&D expenditure % GDP. • School enrolment, secondary (%gross). • Cost of business start-up procedure (% of GNI per capita). • Regulatory quality. • Openness (Trade (% of GDP). • Total natural resources rents (% of GDP). | • High-tech export as % total manufacturing exports. |
Jon M. Zabala-Iturriagagoitia et al., 2007 | DEA | • Property right; medium-tech industries. • Public R&D expenditure R&D. • Business R&D expenditure. • The percentage of the population between 25 and 64 years of age with a higher education | • Patents. • GDP per capita. |
Kou et al., 2016 | Multi-period and multi-division systems (MPMDS), Dynamic network DEA (DN–DEA) | • R&D expenditure. • R&D personnel. • S&T papers. • Technology import. | • Export of high -tech products. • GDP of employment (The ratio of gross domestic product (GDP) to total employment in the economy). |
Nasierowski & Arcelus, 2003 | Two step- DEA (CCR) input-orientation + PCA (two principal components analysis) | • Imports of goods and commercial services. • Gross domestic expenditure on research. • Employment in R&D. • Total educational expenditures. | • External patents by resident. • Patents by a country’s residents. • National productivity. |
Furman et al., 2002 | Modeling national innovative capacity based on Romer formulation | • Patents. • Patent per million. • R&D expenditure. • Openness. • Education expenditure. • R&D spending by private sector. • R&D spending by Universities. | • Publications. • GDP. • Capital Stock. • High-tech exports. |
Crespo & Crespo, 2016 | Fuzzy-set qualitative comparative analysis. | • Institutions. • Human capital and research. • Infrastructure. • Market sophistication. • Business sophistication. | |
Filippetti & Peyrache, 2011 | DEA and PCA | • Triadic patents. • Business R&D (BERD). • Total researchers in R&D (FTE). • Scientific and technical articles. • Public R&D. • Higher Education Expenditure on R&D. • Labour force with tertiary education. | |
Zhao et al., 2015 | Ordinal Multidimensional Scaling and Cluster analysis | – | |
Wang, Zhao, & Zhang, 2016 | The time lags effects of innovation input on output in the NISs | • Researchers in R&D (per million people). • R&D expenditure (% of GDP). • Regulatory quality. • University-industry research collaboration. • Patent applications, residents. | |
Sesay et al., 2018 | Dynamic Panel Data Analysis NIS ➔ Economic Growth | • University enrolment rate for science and engineering students. • government research and development expenditure. • High-tech export. • Total number of patents. • Scientific personnel. • Scientific and technical journal articles. • Economic freedom. | |
Proksch et al., 2017 | Fuzzy-set qualitative comparative analysis (fsQCA) | • International patents per million inhabitants. • GDP per capita. • Stock of international patents. • Aggregate R&D expenditures. • Openness. • Strength of protection for IP. • Share of government expenditure on higher education. • Stringency of antitrust policies. • Specialization degree. • New business registered. • Capital formation. | |
Pires & Garcia, 2012 | Stochastic Frontier Analysis (SFA) productivity analysis | • GDP growth. • Capital accumulation. • Labour expansion. • Change in GDP per worker. • R&D expenditures. • Average years of schooling of population over 25 years. | |
Ivanova et al., 2017 | Economic complexity index; Patent complexity index; Triple-helix complexity index | Patent and groups of products. | |
Altuntas et al., 2016 | A fuzzy-logic based data-mining approach to assess innovation capability of manufacturing systems | – | |
Samara et al., 2012 | The paper analyses the impact of innovation Policies on the NIS performance based on system dynamics (SD) | • Public Expenditure on R&D. • Private Expenditures on R&D. • Patent. • Trademark. • Total public education expenditure. • Population with tertiary education per 100 population aged. • Doctorate graduates per 1000 population aged. • Government debt (% GDP). • Total tax rate. • Number of procedures required to start a business. • Venture capital. • Employment in knowledge intensive services (% of workforce). | |